Diagnostic Analysis of Patients with Essential Hypertension Using Association Rule Mining

Abstract

Objectives: The purpose of this study was to analyze the records of patients diagnosed with essential hypertension using association rule mining (ARM). Methods: Patients with essential hypertension (ICD code, I10) were extracted from a hospital's data warehouse and a data mart constructed for analysis. Apriori modeling of the ARM method and web node in the Clementine 12.0 program were used to analyze patient data. Results: Patients diagnosed with essential hypertension totaled 5,022 and the diagnostic data extracted from those patients numbered 53,994. As a result of the web node, essential hypertension, non-insulin dependent diabetes mellitus (NIDDM), and cerebral infarction were shown to be associated. Based on the results of ARM, NIDDM (support, 35.15%; confidence, 100%) and cerebral infarction (support, 21.21%; confidence, 100%) were determined to be important diseases associated with essential hypertension. Conclusions: Essential hypertension was strongly associated with NIDDM and cerebral infarction. This study demonstrated the practicality of ARM in co-morbidity studies using a large clinic database. Keywords: Hypertension, Diagnosis, Data Mining